Hypercube concurrent processor implementation of a position invariant object classifier

摘要

A position-invariant fast object classification scheme is described. The grey-level image of objects is converted to binary form and a parallel region growing techniques is employed to detect objects. A 2-D fast Fourier transform (FFT) is applied to each object region after translating the origin of the image co-ordinate system to the object centre and aligning the image co-ordinate axes with the object principal axes. The first five components from the principal lobe of the Fourier spectrum of each object are selected as characteristic features for minimum-distance object classification. For time efficiency, region growing and 2-D FFT computations were performed on a 16-node hypercube processor.